Nonparametric joint shape learning for customized shape modeling

Gozde Unal*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

We present a shape optimization approach to compute patient-specific models in customized prototyping applications. We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation. The coupled shape prior forces with the help of application-specific data forces and smoothness forces drive a surface deformation towards a desired output surface. We demonstrate the usefulness of the method for generating customized shape models in applications of hearing aid design and pre-operative to intra-operative anatomic surface estimation.

Original languageEnglish
Pages (from-to)298-307
Number of pages10
JournalComputerized Medical Imaging and Graphics
Volume34
Issue number4
DOIs
Publication statusPublished - Jun 2010
Externally publishedYes

Funding

FundersFunder number
National Center for Research ResourcesU41RR019703

    Keywords

    • Customized shape modeling
    • Hearing aid design
    • Joint shape prior
    • Nonparametric shape density
    • Pre-operative and intra-operative shape modeling
    • Shape estimation
    • Variational shape optimization

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